Candidate biomarkers for diagnosis and prognosis of prostate cancer based on bioinformatics methods

Abstract

Background: Prostate cancer (PCa) is one of the most commonly diagnosed cancers and the fifth leading cause of cancer death in men. In this study, candidate biomarkers related to the diagnosis and prognosis of PCa were identified using bioinformatics approach. Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues were screened using the R software. Enrichment analysis of the DEGs was performed to determine their functions and related pathways. PPI network was constructed, and 10 hub genes were screened using the STRING database and Cytoscape software. Weighted gene co-expression network analysis (WGCNA) was performed to extract key module genes, from which 5 key genes were identified by Venn diagram. Receiver operating characteristic (ROC) analysis was performed to identify the diagnostic value of the key genes, and their prognostic value was verified via survival analysis, which was further validated using the Human Protein Atlas. Results: We identified 661 DEGs (249 upregulated and 412 downregulated) between the PCa group and healthy controls. Overlap of PPI and WCCNA networks identified 5 key genes: BUB1B, HMMR, RRM2, CCNA2 and MELK, as candidate biomarkers for PCa. Although ROC analysis suggested that these genes had diagnostic potential in PCa, survival analysis showed that RRM2 and BUB1B were significantly associated with PCa prognosis. Conclusion: Our results showed that BUB1B, HMMR, RRM2, CCNA2 and MELK could be diagnostic biomarkers for PCa, while RRM2 and BUB1B were also associated with prognosis and could be potential therapeutic targets for PCa.